Pramod Gupta – författare

This book provides an introduction to machine learning and cloud computing, both from a conceptual level, along with their usage with underlying infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and high security, preparing readers to select and make use of appropriate techniques. Important topics are demonstrated using real applications and case studies.  

This book introduces the essentials of Python for the emerging fields of Machine Learning (ML) and Artificial Intelligence (AI). The authors explore the use of Python’s advanced module features and apply them in probability, statistical testing, signal processing, financial forecasting, and various other applications. This includes mathematical operations with array data structures, Data Manipulation, Data Cleaning, machine learning, Data pipeline, probability density functions, interpolation, visualization, and other high-performance benefits using the core scientific packages NumPy, Pandas, SciPy, Sklearn/Scikit learn and Matplotlib. Readers will gain a deep understanding with problem-solving experience on these powerful platforms when dealing with engineering and scientific problems related to Machine Learning and Artificial Intelligence. Several examples of real problems using these techniques are provided along with examples. The authors also focus on the best practices in theindustry on using Python for AI and ML. Deployment on a cloud infrastructure is described in detail (with code) to emphasize real scenarios.

This book provides an introduction to machine learning, security and cloud computing, from a conceptual level, along with their usage with underlying infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and high security, preparing readers to select and make use of appropriate techniques. Important topics are demonstrated using real applications and case studies.  

World population is increasing at an alarming rate and this has resulted in increasing tremendously the demand for tree products such as wood for construction materials, fuel and paper, fruits, oils and medicines etc. This has put immense pressure on the world’s supplies of trees and raw material to industry and will continue to do so as long as human population continues to grow. Also, the quality of human diet, especially nutritional components, is adversely affected due to limited genetic improvement of most of fruit trees. Thus there is an immediate need to increase productivity of trees. Improvement has been made through conventional breeding methods, however, conventional breeding is very slow due to long life cycle of trees. A basic strategy in tree improvement is to capture genetic gain through clonal propagation. Clonal propagation via organogenesis is being used for the production of selected elite individual trees. However, the methods are labour intensive, costly, and produce low volumes. Genetic gain can now be captured through somatic embryogenesis. Formation of embryos from somatic cells by a process resembling zygotic embryogenesis is one of the most important features of plants. In 1958, Reinert in Germany and Steward in USA independently reported somatic embryogenesis in carrot cultures. Since then, tremendous progress in somatic embryogenesis of woody and non-woody plants has taken place. It offers a potentially large-scale propagation system for superior clones.